DNA (CpG) methylation as an epigenetic determinant of transcriptional gene silencing has been studied extensively in many disease models (ageing and cancer in particular). The long-term objective of this population-based and multidisciplinary study is to define how infection with human immunodeficiency virus type 1 (HIV-1) alters the CpG methylation profiles at 13 polymorphic and/or methylation-sensitive loci. Within the context of gene expression in relation to HIV-1 transmission, pathogenesis, and drug resistance, the study aims to test four hypotheses through systematic analyses of CpG methylation patterns at the loci encoding 1) HIV-1 receptor CD4, coreceptors CCR5 and CXCR4, and coreceptor ligands RANTES and SDF-1; 2) key immunoregulatory cytokines including interferon gamma, interleukin 2 (IL-2), IL-4, IL-6, IL-10, and tumor necrosis factor (TNF) alpha; 3) thymidine kinase-1 (TK-1) and multidrug resistance glycoprotein (MDR-1) responsible for drug metabolism and transport. Our work will focus on subjects of African ancestry from the Reaching for Excellence in Adolescent Care and Health (REACH) cohort and the Zambia-UAB HIV Research Project (ZUHRP). Participants with longitudinal clinical data and biological specimens (serum, plasma, DNA and cells) will be selected for three major comparisons: i) 80-100 ZUHRP seroconverters before vs. after HIV-1 infection, ii) 80-100 REACH seropositives before vs. after effective antiretroviral therapy (ART), and iii) HIV-1 seropositive and ART-free REACH and ZUHRP controller (n=80-100) with the lowest plasma viral load (VL) vs. non-controllers (n=80-100) with the highest VL (matched by age, sex, cohort, and ethnicity). A combination of molecular techniques will be used to classify both genetic and epigenetic polymorphisms; gene expression (mRNA production and/or protein secretion) will be quantified by reverse transcription polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). Other immunogenetic factors identified earlier in these cohorts will serve as covariates. Statistical models will explore individual as well as interactive effects of genetic and epigenetic variants. Collectively, data derived from this study should allow a comprehensive dissection of the complex and evolving virus-host interplays and ultimately enhance the design of interventions based on existing and often clinically tested antagonists, inhibitors, monoclonal antibodies, plasmid constructs, and recombinant products.